Committed Ice Loss in the European Alps Until 2050 Using a Deep-Learning-Aided 3D Ice-Flow Model With Data Assimilation
Abstract
Modeling the short-term (<50 years) evolution of glaciers is difficult because of issues related to model initialization and data assimilation. However, this timescale is critical, particularly for water resources, natural hazards, and ecology. Using a unique record of satellite remote-sensing data, combined with a novel optimisation and surface-forcing-calculation method within the framework of the deep-learning-based Instructed Glacier Model, we are able to ameliorate initialization issues. We thus model the committed evolution of all glaciers in the European Alps up to 2050 using present-day climate conditions, assuming no future climate change. We find that the resulting committed ice loss exceeds a third of the present-day ice volume by 2050, with multi-kilometer frontal retreats for even the largest glaciers. Our results show the importance of modeling ice dynamics to accurately retrieve the ice-thickness distribution and to predict future mass changes. Thanks to high-performance GPU processing, we also demonstrate our method's global potential. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000647254Publication status
publishedExternal links
Journal / series
Geophysical Research LettersVolume
Pages / Article No.
Publisher
American Geophysical UnionOrganisational unit
09599 - Farinotti, Daniel / Farinotti, Daniel
Funding
869304 - PROjecTing sEa-level rise : from iCe sheets to local implicaTions (EC)
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